Compressive Sensing and Hierarchical Clustering for Microarray Data with Missing Values

Commonly, in gene expression microarray measurements multiple missing expression values are generated, and the proper handling of missing values is a critical task. To address the issue, in this paper a novel methodology, based on compressive sensing mechanism, is proposed in order to analyze gene expression data on the basis of topological characteristics of gene expression time series. The approach conceives, when data are recovered, their processing through a non-linear PCA for dimensional reduction and a Hierarchical Clustering Algorithm for agglomeration and visualization. Experiments have been performed on the yeast Saccharomyces cerevisiae dataset by considering different percentages of information loss. The approach highlights robust performance when high percentage of loss of information occurs and when few sampling data are available.